Modern computing systems of all kinds accumulate various data at an almost unimaginable rate. Alongside the advances in technology that make such storage possible has grown a realisation that buried within this mass of data there may exist some knowledge of considerable value. This could be information critical for a company's business success or something leading to a scientific or medical discovery or breakthrough. Most data is simply stored and never examined, but machine-learning technology has the potential to extract knowledge of value (i.e. data mining).

• Part I: Knowledge discovery and data mining in theory
• Chapter 1: Estimating concept difficulty with cross entropy
• Chapter 2: Analysing outliers by searching for plausible hypotheses
• Chapter 3: Attribute-value distribution as a technique for increasing the efficiency of data mining
• Chapter 4: Using background knowledge with attribute-oriented data mining
• Chapter 5: A development framework for temporal data mining
• Chapter 6: An integrated architecture for OLAP and data mining
• Part II: Knowledge discovery and data mining in practice
• Chapter 7: Empirical studies of the knowledge discovery approach to health-information analysis
• Chapter 8: Direct knowledge discovery and interpretation from a multilayer perceptron network which performs low-back-pain classification
• Chapter 9: Discovering knowledge from low-quality meteorological databases
• Chapter 10: A meteorological knowledge-discovery environment
• Chapter 11: Mining the organic compound jungle - a functional programming approach
• Chapter 12: Data mining with neural networks - an applied example in understanding electricity consumption